---
title: "Attrition Predictor Analysis"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: fill
social: ["github","linkedin", "twitter", "facebook", "menu"]
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(DT)
library(rpivotTable)
library(ggplot2)
library(plotly)
library(dplyr)
library(openintro)
library(highcharter)
library(ggvis)
library(broom)
```
```{r, message=FALSE}
#"data" has categorical data converted to numerical
# "data2" has a mixed data types
data <- read.csv("~/Documents/Projects/SMU/rProjects/CaseStudy_02/CaseStudy_02/NumData.csv")
data2 <- read.csv("~/Documents/Projects/SMU/rProjects/CaseStudy_02/CaseStudy_02/CaseStudy2-data.csv")
#View(data)
#str(data)
```
```{r echo=FALSE}
# embed all Rmd and csv files
xfun::embed_files(list.files('.', '[.](Rmd|csv)$'))
```
```{r}
mycolors <- c("blue", "#FFC125", "darkgreen", "darkorange", "darkblue")
```
Interactive Data Visualization
=====================================
Row
-------------------------------------
### Attrition Count
```{r}
valueBox(paste("Attrition"),
color = "darkblue")
#unique.(data$Attrition)
#data %>%
# summarise(Attrition = n())
```
### Number of Employee Data Reviewed
```{r}
valueBox(length(data$Attrition),
icon = "fa-users")
```
### **Monthly Income**
```{r}
gauge(round(min(data$MonthlyIncome),
digits = 2),
min = 0,
max = 25000,
label = "Min Monthly Income",
gaugeSectors(success = c(15000, 25000),
warning = c(5000,15000),
danger = c(0, 5000),
colors = c("green", "yellow", "red")))
```
```{r}
gauge(round(mean(data$MonthlyIncome),
digits = 2),
min = 0,
max = 25000,
label = "Avg Monthly Income",
gaugeSectors(success = c(15000, 25000),
warning = c(5000,15000),
danger = c(0, 5000),
colors = c("green", "yellow", "red")))
```
```{r}
gauge(round(max(data$MonthlyIncome),
digits = 2),
min = 0,
max = 25000,
label = "Max Monthly Income",
gaugeSectors(success = c(15000, 25000),
warning = c(5000,15000),
danger = c(0, 5000),
colors = c("green", "yellow", "red")))
```
### Yes on Attrition
```{r, Yes}
valueBox(sum(data$Attrition == "Yes"),
icon = 'fa-user-o')
```
### No on Attrition
```{r, No}
valueBox(sum(data$Attrition == "No"),
icon = 'fa-user')
```
Row
-------------------------------------------------
### **Plotly Bar Plot **
```{r}
#summary(data2$Attrition)
#str(data2$Attrition)
#p0 <- ggplot2::data2 %>% sum(JobRole, Attrition) %>%
# plot_ly(x = ~JobRole, y = ~n, color = ~Attrition)
p1 <- data2 %>%
group_by(JobRole) %>%
summarise(count = n()) %>%
plot_ly(x = ~JobRole,
y = ~count,
color = 'blue',
type = 'bar') %>%
layout(xaxis = list(title = "Job Role"),
yaxis = list(title = 'Count'))
p1
#names(data2)
```
### **Plotly Box Plot of Job Role & Income**
```{r}
data2 %>%
group_by(JobRole) %>%
plot_ly(y = ~JobRole, x = ~MonthlyIncome, type = "box", boxpoints = "outliers", color = ~JobRole, jitter = 0.3, pointpos = 0, orientation = "h")
```
Interactive Scatter Plots
=====================================
Row
-------------------------------------
### **Monthly Income v Age**
```{r}
j <- loess(MonthlyIncome ~ Age, data = data2)
p2 <- plot_ly(data = data2, x = ~Age,
y = ~MonthlyIncome,
color = as.factor(data2$Attrition),
type = 'scatter', alpha = 0.9) %>%
add_lines(y= ~fitted(loess(data2$MonthlyIncome ~ data2$Age)),
line = list(color = 'rgba(7,164,181,1)'),
name = "Loess Smoother") %>%
add_ribbons(data = augment(j),
ymin = ~.fitted - 1.96 * .se.fit,
ymax = ~.fitted + 1.96 * .se.fit,
line = list(color = 'rgba(7, 164, 181, 0.05)'),
fillcolor = 'rgba(7, 164, 181, 0.2)',
name = "Standard Error") %>%
layout(xaxis = list(title = "Age"),
yaxis = list(title = 'Monthly Income'))
p2
```
### **Monthly Income v Time in C. Role**
```{r}
l <- loess(MonthlyIncome ~ YearsInCurrentRole, data = data2)
p3 <- plot_ly(data = data2, x = ~YearsInCurrentRole,
y = ~MonthlyIncome,
color = as.factor(data2$Attrition),
type = 'scatter', alpha = 0.9) %>%
add_lines(y= ~fitted(loess(data2$MonthlyIncome ~ data2$YearsInCurrentRole)),
line = list(color = 'rgba(7,164,181,1)'),
name = "Loess Smoother") %>%
add_ribbons(data = augment(l),
ymin = ~.fitted - 1.96 * .se.fit,
ymax = ~.fitted + 1.96 * .se.fit,
line = list(color = 'rgba(7, 164, 181, 0.05)'),
fillcolor = 'rgba(7, 164, 181, 0.2)',
name = "Standard Error") %>%
layout(xaxis = list(title = "Years In Current Role"),
yaxis = list(title = 'Monthly Income'))
p3
```
### **Monthly Income v Time At Company **
```{r}
k <- loess(MonthlyIncome ~ YearsAtCompany, data = data2)
p5 <- plot_ly(data = data2, x = ~YearsAtCompany,
y = ~MonthlyIncome,
color = as.factor(data2$Attrition),
type = 'scatter', alpha = 0.9) %>%
add_lines(y= ~fitted(loess(data2$MonthlyIncome ~ data2$YearsAtCompany)),
line = list(color = 'rgba(7,164,181,1)'),
name = "Loess Smoother") %>%
add_ribbons(data = augment(k),
ymin = ~.fitted - 1.96 * .se.fit,
ymax = ~.fitted + 1.96 * .se.fit,
line = list(color = 'rgba(7, 164, 181, 0.05)'),
fillcolor = 'rgba(7, 164, 181, 0.2)',
name = "Standard Error") %>%
layout(xaxis = list(title = "Years At Company"),
yaxis = list(title = 'Monthly Income'))
p5
```
Row
---------------------------------------------
Comparison of Monthly Income with other variables.
Age
Time in Current Role
Time with Company
```{r}
### **Box Plot of Top State**
#data2 %>%
# group_by(JobRole) %>%
# ggvis(~JobRole, ~MonthlyIncome, fill = ~JobRole) %>%
# layer_boxplots()
```
Data Table
========================================
```{r}
datatable(data2,
caption = "Attrition Data",
rownames = TRUE,
filter = "top",
options = list(pageLength = 25))
```
Pivot Table - Numeric
=========================================
```{r}
rpivotTable(data,
aggregatorName = "Count",
cols= "Attrition",
rows = "JobRole",
rendererName = "Heatmap")
```
Pivot Table - Categorical
=========================================
```{r}
rpivotTable(data2,
aggregatorName = "Count",
cols= "Attrition",
rows = "JobRole",
rendererName = "Heatmap")
```
About Report
========================================
Project By:
Heindel Adu, Stephen Johnson, Ross Fu, Anthony Yeung
Classification: CONFIDENTIAL!